Q-omics provides the consensus-scored IKZF2 profile across patient tissues and cancer cell-line models. IKZF2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, IKZF2 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, IKZF2 RNA expression shows 19,726 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, and UVM as cancer lineages where IKZF2 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for IKZF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IKZF2 survival associations across molecular data types. IKZF2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (9) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IKZF2 RNA expression–survival associations across cancer types. High IKZF2 expression shows unfavorable associations in LGG, but favorable associations in HNSC, SKCM, KIRC, ACC and BRCA. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for IKZF2 RNA expression.
This table summarizes IKZF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for IKZF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IKZF2 shows lower tumor expression in HNSC and KICH and higher tumor expression in LUAD, BLCA, THCA and BRCA. The HNSC box plot shows higher IKZF2 RNA expression in normal versus tumor tissue (log2 FC = −1.549, t-test p < 0.001).
This table shows molecular features associated with IKZF2 in patient tissues and cancer cell lines. In patient samples, IKZF2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, IKZF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Leukemia.
